animal
Animal, page 1 of 11 © The Animal Consortium 2015
doi:10.1017/S1751731115002700
Ossification score is a better indicator of maturity related changes
in eating quality than animal age
S. P. F. Bonny1,7†, D. W. Pethick1, I. Legrand2, J. Wierzbicki3, P. Allen4, L. J. Farmer5,
R. J. Polkinghorne6, J.-F. Hocquette7,8 and G. E. Gardner1
1
School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA 6150, Australia; 2Institut de l’Elevage, Service Qualite’ des Viandes, MRAL, 87060
Limoges Cedex 2, France; 3Polish Beef Association Ul. Kruczkowskiego 3, 00-380 Warszawa, Poland; 4Teagasac Food Research Centre, Ashtown, Dublin 15, Ireland;
5
Agri-Food and Biosciences Institute, Newforge Lane, Belfast BT9 5PX, UK; 6431 Timor Road, Murrurundi, NSW 2338, Australia; 7INRA, UMR1213, Recherches sur les
Herbivores, F-63122 Saint Genès Champanelle, France; 8Clermont Université, VetAgro Sup, UMR1213, Recherches sur les Herbivores, F-63122 Saint Genès
Champanelle, France
(Received 9 June 2015; Accepted 30 October 2015)
Ossification score and animal age are both used as proxies for maturity-related collagen crosslinking and consequently decreases in
beef tenderness. Ossification score is strongly influenced by the hormonal status of the animal and may therefore better reflect
physiological maturity and consequently eating quality. As part of a broader cross-European study, local consumers scored
18 different muscle types cooked in three ways from 482 carcasses with ages ranging from 590 to 6135 days and ossification
scores ranging from 110 to 590. The data were studied across three different maturity ranges; the complete range of maturities,
a lesser range and a more mature range. The lesser maturity group consisted of carcasses having either an ossification score of
200 or less or an age of 987 days or less with the remainder in the greater maturity group. The three different maturity ranges
were analysed separately with a linear mixed effects model. Across all the data, and for the greater maturity group, animal age
had a greater magnitude of effect on eating quality than ossification score. This is likely due to a loss of sensitivity in mature
carcasses where ossification approached and even reached the maximum value. In contrast, age had no relationship with eating
quality for the lesser maturity group, leaving ossification score as the more appropriate measure. Therefore ossification score is
more appropriate for most commercial beef carcasses, however it is inadequate for carcasses with greater maturity such as cull
cows. Both measures may therefore be required in models to predict eating quality over populations with a wide range in maturity.
Keywords: beef quality, ossification score, age, maturity, consumer testing
Implications
Linking producer payments to eating quality, in combination
with yield, is vital for improving the beef industry and
delivering a quality product to consumers. Both ossification
score and animal age are used as indications of age-related
decreases in eating quality and tenderness. This work
demonstrates that ossification score is a more accurate
predictor of eating quality for less mature animals, commonly
used for beef production. However it is limited in its use for
mature animals such as cull cows, for which animal age is a
more appropriate measure.
Introduction
For beef to remain competitive in the market place, the industry
must address consumer demands. Variable eating quality, in
†
E-mail: [email protected]
particular consumers being unable to identify beef of a
consistent or desired tenderness, is seen as a major factor in the
global decline in beef consumption (Morgan et al., 1991;
Polkinghorne et al., 2008b). The Meat Standards Australia
(MSA) system has addressed this issue through a prediction
model using carcass traits and pre-slaughter guidelines to supply beef to consumers with a guaranteed tenderness and eating
quality (Polkinghorne et al., 2008a and 2008b; Watson et al.,
2008a and 2008b). A similar system guaranteeing beef eating
quality would be well accepted by European beef consumers
(Verbeke et al., 2010), and would also enable products within
such a system to command a premium price (Lyford et al.,
2010).
The Australian MSA system relies on a maturity estimate
as an essential part of the quality prediction (Polkinghorne
et al., 2008b). Animal maturity has been well established as
a valuable indicator of eating quality through its negative
relationship with tenderness (Shorthose and Harris, 1990;
1
Bonny, Pethick, Legrand, Wierzbicki, Allen, Farmer, Polkinghorne, Hocquette and Gardner
Schonfeldt and Strydom, 2011). As an animal matures
crosslinks develop within all collagen, including the collagen
present in muscle tissue. These crosslinks increase the
thermal stability and decrease the solubility of the collagen
matrix (Weston et al., 2002). With the increased stability
more collagen survives the cooking process intact, acting to
reduce the tenderness of the subsequent product (Weston
et al., 2002). Therefore meat becomes tougher as animals
mature.
Within Europe, animal maturity is estimated using animal
age, with certain ages set as thresholds to differentiate beef
into different markets. Alternatively, the MSA system and the
American United Stated Department of Agriculture (USDA)
system use ossification score as an indicator of animal
maturity in the prediction of beef eating quality (USDA, 1997;
Polkinghorne et al., 2008b). Ossification score is measured
by a visual assessment of the degree of calcification in
the cartilage of the sacral and dorsal vertebrae (USDA, 1997).
Animal age and ossification score in slaughter cattle
in the USA have a positive relationship (Shackelford et al.,
1995) with one population of cattle having a simple correlation coefficient of r = 0.64 (Raines et al., 2008). However
this relationship has not been examined in carcasses with
greater maturity. Ossification score reaches its maximum
score of 600 as animals reach 8 years of age (Raines et al.,
2008). As such it would be expected that for animals with
greater maturities there would be a reduced or no correlation
between animal age and ossification score.
The correlation between animal age and ossification score,
and the correlations between these measures and eating
quality are affected by several factors. The rate of ossification
and hence ossification score at any given age is strongly
influenced by the hormonal status of an animal, particularly
through the hormone oestrogen (Field et al., 1997; Scheffler
et al., 2003). Factors such as sex, castration, hormonal
growth promotants and parity status all influence oestrogen
levels and therefore ossification score at any given age
(Waggoner et al., 1990; Field et al., 1996; Scheffler et al.,
2003). In contrast animal age is a simple linear measurement
and is not affected by physiological processes such as age at
maturity, pregnancy and lactation. Through its close relationship with physiological processes, ossification score has
a better capacity than animal age to reflect the physiological
maturity of an animal (Field et al., 1997) and consequently
the maturity related decrease in tenderness (Weston et al.,
2002). However this effect may be limited when ossification
scores approach and reach the upper limit in groups with
greater maturities.
Therefore we hypothesize that European cattle will
demonstrate a positive relationship between animal age and
ossification score, particularly for animals with lesser
maturity, and for this relationship to be reduced for animals
with greater maturity. We also hypothesize that ossification
score will be a better predictor of eating quality than
chronological age for carcasses with lesser maturity, whereas
animal age will be a better predictor of eating quality in
carcases of greater maturity.
2
Material and methods
Animals and muscle samples
The cattle were chosen randomly at commercial abattoirs on
the day of sampling to reflect the different commercial
production practices within France, Poland, Ireland and
Northern Ireland. As a result the carcasses had an uneven
distribution of breed, age, sex and carcass composition. The
Polish carcasses were processed at a number of facilities
distributed across the country. The Irish carcasses were processed at two commercial abattoirs and one pilot scale
abattoir. The French carcasses were processed at a single
facility in the West of France. The carcasses from Northern
Ireland were processed at five different facilities distributed
across the region.
The cattle were slaughtered commercially according to
standard practice in each country. Animal age in days was
determined from the legally documented birth dates and
slaughter dates, as required in the European Union. All
carcasses were graded by personnel trained in MSA and
USDA meat grading according to standard MSA protocols for
characteristics such as ossification (an estimate of maturity),
marbling and ultimate pH. Ultimate pH was recorded at 24 h
post slaughter. Ossification score is measured following the
guidelines from the USDA. It is a measure of the calcification
in the spinous processes in the sacral, lumbar and thoracic
vertebrae and provides a scale between 100 and 590 in
increments of 10 for MSA which is an assessment of
physiological age of a bovine carcass (AUS-MEAT, 2005).
Marbling score is a measure of the fat deposited between
individual fibres in the rib eye muscle ranging from 100 to
1100 in increments of 10. Marbling is assessed at the
quartering site of the chilled carcass and is calculated by
evaluating the amount, piece size and distribution of
marbling in comparison to the MSA reference standards
(AUS-MEAT, 2005; MLA 2006).
All cattle were growth-promotant free as these are
prohibited in the European Union. There was a wide range in
age and ossification score, though the distribution was
heavily weighted towards carcasses of lesser maturity
(Table 1). There was a wide range in the other carcass traits
measured such as marbling score and carcass weight
(Table 1). There were four different cooking methods,
grill, roast, slow cook and Korean BBQ (barbeque). All
cooking methods had muscle samples from carcasses with a
wide range in age and ossification score. Some carcasses
and muscle samples were prepared using more than one
cooking method (Table 2). The postmortem ageing period of
the muscle samples ranged between 5 and 35 days, and
varied between countries, cooking method and other effects
in the study (Table 3). Some muscle samples had multiple
ageing periods. All muscles were prepared using the grill
cooking method as this is the method most commonly
investigated in the literature. In order to examine the other
cooking methods, subsets of the muscle samples had
portions which were also prepared using one of the other
cooking methods.
Ossification is better than age for beef quality
Table 1 Number of bovine carcasses and the raw maximum, minimum, mean and standard deviation for the covariates
measured in this study, by maturity range
Carcasses
Mean
SD
Minimum
Maximum
482
482
475
481
482
396
421
906
195
5.60
329
334
63.6
72.3
730
102
0.20
53.9
115
14.2
19.4
369
110
5.33
188
100
25
30
6133
590
7.15
515
820
115
140
48
48
48
48
48
30
47
2774
469
5.57
355
418
55.8
74.3
1152
118
0.09
37.0
155
13.0
23.8
913
210
5.43
304
200
25
30
6133
590
5.76
452
780
80
110
434
434
427
433
434
366
374
699
165
5.60
326
325
64.2
72.0
132
29.6
0.21
54.7
106
14.1
18.8
369
110
5.33
188
100
30
30
1038
350
7.15
515
820
115
140
Alla
Aged
Ossification score
Ultimate pH
Carcass weight
Marbling score
Hump height
Eye muscle area
Greater maturityb
Aged
Ossification score
Ultimate pH
Carcass weight
Marbling score
Hump height
Eye muscle area
Lesser maturityc
Aged
Ossification score
Ultimate pH
Carcass weight
Marbling score
Hump height
Eye muscle area
All other measures were recorded as standard Meat Standards Australia (MSA) measurements by trained graders.
The number of carcasses varies for each measure because not all measurements were recorded for all carcasses.
a
All = the full range of bovine carcasses.
b
Greater maturity = carcasses not classified as having lesser maturity.
c
Lesser maturity = ossification score ≤200 or age ≤987 days.
d
Age = chronological age in days.
Table 2 Number of bovine carcasses, number of samples from those bovine carcasses and the raw maximum, minimum, mean
and standard deviation of animal age (days) and ossification score by cooking method
Cooking Method
Grill
Agea
Ossification2
Roast
Agea
Ossificationb
Slowcook
Agea
Ossificationb
Korean BBQ
Agea
Ossificationb
Carcasses
Samples
Mean
SD
Minimum
Maximum
472
472
4333
4333
912
195
736
103
369
110
6133
590
296
296
2205
2205
736
176
387
59.1
369
110
4695
590
30
30
180
180
1145
259
1066
138
369
130
4695
590
20
20
134
134
609
185
26.2
20.0
559
140
646
230
BBQ = barbeque.
a
Chronological age in days.
b
Ossification score was recorded as standard MSA (Meat Standards Australia) assessments by trained graders.
As expected in an observational study there was an
uneven distribution of cattle and samples amongst all the
effects controlled for in this study. Animal breed was divided
into three categories or classes; beef breeds, dairy breeds and
crosses between the beef and dairy breeds. Eighteen different muscles were represented in the 6852 different samples;
however the number and type of muscles sampled varied
between carcasses and other factors in the study (Table 3).
3
Bonny, Pethick, Legrand, Wierzbicki, Allen, Farmer, Polkinghorne, Hocquette and Gardner
Table 3 Number of bovine carcasses from which specific muscles were sampled, within hang method, sex, cooking method and breed purpose,
by muscle
Hang
Muscle
Blade1
Chuck2
Chuck Tender3
Cube roll a4
Cube roll b5
Silverside a6
Knuckle a7
Knuckle b8
Silverside b9
Blade10
Rump cap11
Rump tail12
Eye of rump centre13
Eye of rump side14
Shortloin15
Tenderloin16
Topside a17
Topside b18
Sex
Cook
Class
Achilles
Tender stretch
Bull
Female
Steer
Grill
Roast
Slowcook
Korean BBQ
Cross
Dairy
Beef
75
39
12
21
29
54
111
47
142
50
86
18
262
133
320
148
60
309
–
–
–
–
–
55
41
18
82
–
99
–
193
168
256
16
63
150
36
29
12
21
24
33
58
40
90
25
18
16
90
71
91
50
24
63
39
10
–
–
5
29
44
12
92
25
35
2
122
46
162
114
37
165
–
–
–
–
–
36
32
13
42
–
72
–
146
110
213
–
35
121
27
–
12
12
12
43
90
27
223
48
67
12
324
122
464
164
27
343
25
9
8
9
17
77
54
29
117
2
59
12
166
155
236
46
93
179
30
30
–
–
–
–
15
15
30
–
–
–
–
–
30
–
–
30
20
–
–
–
–
–
–
–
18
–
–
–
20
–
20
18
–
18
50
17
5
7
13
48
25
20
63
16
4
9
54
23
108
52
7
108
25
22
7
14
16
16
46
21
80
28
37
9
116
64
153
58
21
95
–
–
–
–
–
34
63
24
781
6
84
–
188
140
205
54
68
146
BBQ = barbeque; Cross = beef and dairy breed cross; Dairy = dairy breed; Beef = beef breed.
1
m. triceps brachii caput longum; 2m. serratus ventralis cervicis; 3m. supraspinatus; 4m. longissimus thoracis et lumborum; 5m. spinalis dorsi; 6m. semitendinosus;
7
m. rectus femoris; 8m. vastus lateralis; 9m. biceps femoris; 10m. infraspinatus; 11m. biceps femoris; 12m. tensor fasciae latae; 13m. gluteus medius; 14 m. gluteus medius;
15
m. longissimus thoracis et lumborum; 16m. psoas major; 17m. adductor femoris; 18m. semimembranosus.
Meat preparation
Meat preparation and consumer assessment of eating quality
for the four cooking methods were performed according to
protocols for MSA testing described by (Anonymous, 2008;
Watson, 2008a). In all cases the dissected muscle was
denuded of all fat and epimysium. For the grill and roast
samples a block measuring 75 × 25 × 150 mm was prepared
then commencing at the proximal or anterior end of the
block, five 25 mm thick steaks were cut across the grain
using a cutting guide. The slow cook samples were portioned
into twenty two 21 × 21 × 21 mm cubes. For the Korean BBQ
samples initially a 90 × 20 × 75 mm block with the grain
across the 75 mm line was prepared. After the designated
postmortem ageing period the Korean BBQ samples were
then conditioned to −4°C and sliced to create eleven 4 mm
thick strips sliced across the grain. After their designated
postmortem ageing period samples were then either cooked
and served directly to consumers or frozen at −18°C. Frozen
samples were thawed in a refrigerator at 2°C to 5°C for 24 h
(grill, slow cook), 48 h (roast) or at room temperature 15 to
30 min before serving (Korean BBQ).
Cooking procedures
Grill. Steaks were cooked on a SILEX S-Tronic 163 GR Dual
Contact grill with cast iron plates set at 220°C to 230°C. The
grill was preheated for 45 min and a set of sacrificial steaks
were cooked to commence the cooking cycle and stabilize
temperature recovery. All steaks were cooked for a total
of 300 s (360 s for well done), with the lid up for the first 30 s
4
and the lid closed for the remaining 270 s (330 s for well
done). After cooking steaks were rested for two minutes
before halving and placing on pre-numbered serving plates.
The consistent steak thickness, grill temperature and cooking
time, allowed for an even doneness (internal temperature of
approximately 65°C for medium and 78°C for well-done) for
all samples.
Roast. Roasts were prepared in a commercial gas oven with
sufficient capacity to simultaneously cook all roasts for any
one taste panel. The oven was preheated to 160°C before the
loading of the 42 roast blocks, paired for weight. The oven
was maintained at 160°C during the cooking period and
each roast pair was removed when an internal temperature
of 65°C was reached (78°C for well-done roasts). On removal
the roasts were placed in a bain marie steamer pan and
rested for a minimum of 5 min before trimming to a standard
65 × 65 × 110 mm block. After trimming and during testing
all roasts were kept in bain marie steamer pans which had
been preheated to 48°C. Roasts were served to consumers in
10 mm slices and the carving of each slice was performed
directly before serving, with an internal facing slice removed
immediately before taking the first designated sample. After
each slice was removed the roast block was returned to the
bain marie. The total serving operation took 35 min.
Slow cook. The 22 cubes from each sample were sprayed
liberally with olive oil and browned before cooking in a
preheated stainless steel fry-pan for 90 s. After browning
Ossification is better than age for beef quality
For all cooking methods each consumer received seven
portions: the first portion (a link sample) was derived from
either a generic striploin or rump muscle and expected to be
of average quality – the sensory scores for this portion were
not part of the final statistical analysis. The remaining six
portions were derived from one of the muscle samples
collected. Product order was determined by 6 × 6 Latin
square design and every product occurred an equal number
of times (6) in each presentational position, before and after
each other product in the Latin square. This provides a
balance for frequency, order and carryover effects.
Consumers scored steaks out of 100 for tenderness, juiciness, flavour liking and overall liking, by making a mark on a
100 mm line scale, with the low end of the scale representing
a negative response and the high end of the scale representing a positive response. For a more detailed description
of the testing procedures see Anonymous (2008) and Watson
(2008a).
they were transferred to a bain marie steamer pan containing
300 ml of stock. The stock was made from; 12 l of boiling
water, 1200 g defrosted and sliced frozen onion, 1200 g of
defrosted and sliced frozen carrot, 400 g of fresh machine
chopped celery and 4 level metric tablespoons of fine salt.
The individual bain marie steamer pans were held at a boil
for 30 min before adding the browned sample cubes. The
cubes were then simmered at 93°C to 95°C for 2 h. The
steamer pans were removed after cooking and placed in a
water bath to achieve rapid cooling. The samples were then
held in bain maries set to 48°C for a maximum of 3 h before
serving.
Korean BBQ. Korean BBQ samples were directly cooked and
served by a host seated at a table with five consumers. A metal
disc cooker was mounted on a three ring gas burner with
modified controls to facilitate fine adjustment. A thermocouple
sensor was mounted to the cooking surface to record plate
temperature, which was maintained at between 250°C and
260°C. Single samples were placed on the hotplate in the
serving order and turned as moisture pooled on the surface.
The sample was served to the nominated consumer when
liquid pooled on the second side. The visual indicator combined
with the temperature controlled surface produced a uniform
medium degree of doneness in the cooked beef strip.
Consumer demographics
Consumers scored meat from their country of origin and were
sourced through both commercial consumer testing organizations and local clubs and charities. They were selected to
reflect the general population with the only requirement
being that they considered meat an important part of their
diet. The age ranges and the distribution of the gender of the
consumers for each of the countries is reported in Table 4.
A more detailed description of the demographics of the
French consumers can be found in Legrand et al. (2013).
Consumer panels
Consumer panels for grilled samples were arranged in groups
of 20 consumers, with three such sessions being held
per day. There were 48, 54, 66 and 249 grill sessions in
France, Ireland, Poland and Northern Ireland, respectively.
Roast and slow cook consumer panels were held with groups
of 60 consumers. There were 5, 11 and 58 roast sessions in
Ireland, Poland and Northern Ireland, respectively. There
were five slow cook sessions in Poland. Korean BBQ procedures provide for direct cooking with five consumers served
by each host in a session. There were 30 Korean BBQ
sessions in Ireland.
Meat quality score
Each muscle, cooked with a specified cooking method, was
assessed by 10 individual untrained consumers. The highest
and lowest two scores for each muscle were removed and
the average was calculated for the remaining six scores.
These clipped mean values for tenderness, juiciness, flavour
liking and overall liking were weighted and combined to
create a single meat quality score (MQ4). The weightings
Table 4 Gender and age range (years) of the untrained consumers used in the sensory analysis
Poland
NthIre
Ireland
France
Male
Female
Unreported
Male
Female
Unreported
Male
Female
Unreported
Male
Female
Unreported
<20
20 to 30
35
46
0
471
608
1
1040
823
1
0
0
0
14
13
0
289
117
17
106
169
0
31 to 45
46 to 50
242
384
0
763
1030
0
194
198
16
128
150
1
>50
Unreported
Total
126
361
0
1
1
0
0
0
0
5
0
3
0
1
0
875
1400
1
3653
4662
2
707
561
52
439
519
1
12 872
1850
2809
1
78
90
6
36
50
0
141
156
10
155
136
0
Total
NthIre = Northern Ireland.
Numbers that sit between two columns indicates that the age range of the consumers spans both age groups.
5
Bonny, Pethick, Legrand, Wierzbicki, Allen, Farmer, Polkinghorne, Hocquette and Gardner
were calculated using a discriminant analysis, as performed
by Watson et al. (2008a and 2008b) and are 0.3 ×
tenderness, 0.1 × juiciness, 0.3 × flavour liking and 0.3 ×
overall liking. There is a high correlation between all four
sensory scores with a minimum partial correlation coefficient
between any of the scores of 0.66 calculated on a subset of
the data (Bonny et al., 2015).
Statistical analysis
The effect of both ossification score and age on the composite MQ4 score was assessed across the full ranges of
ossification score and animal age in the data set. The relationship between these two measurements and the MQ4
score was also explored within groups of carcasses with
lesser or greater maturity. The first release of the MSA model
used commercially in Australia disqualified any carcass with
an ossification score >200. To align with this the ‘less
mature group’ was limited to animals that had an ossification score of 200 or less, or were ⩽987 days old at slaughter,
the age equivalent to an ossification score of 200 within this
data set (Figure 1). All carcasses not meeting these criteria
were allocated to the ‘greater maturity range.’ All analyses
were performed on the whole data set and on these two
subgroups using a combination of both correlation analyses
and regression analyses.
Using a data set with only one observation per carcass, the
partial correlation coefficients between ossification score
and age for the three maturity ranges was determined with a
bivariate model (SAS v9.1) accounting for the fixed effects of
country, sex, breed class and kill group, and significant
interactions between these terms. A similar approach using a
bivariate model was then taken to determine the partial
correlation coefficients between the maturity measures and
MQ4 within each of the three maturity ranges, accounting for
the fixed effects of country, sex, breed class and kill group,
600
Ossification
500
400
300
200
and significant interactions between these terms. Initially
this was done within each muscle separately, enabling individual partial correlations to be determined for each muscle.
This process was then repeated, but utilizing data from all
muscles combined in the one data-set with ‘muscle’ included
as a fixed term within the model. This enabled estimation of
the mean partial correlation across all muscles.
The composite score MQ4 was analysed using a linear
mixed effects model (SAS v9.1). Initially, a base model for all
three maturity ranges was established, with the following
fixed effects and all their significant interactions, carcass
hanging method, cooking method, muscle type, sex, country
and breed class. Postmortem ageing period in days was
included as a covariate. Animal identification number within
carcass source country, kill group (animals slaughtered on
the same day at the same abattoir) and consumer country
were included as random terms. The inclusion of animal
identification number assumes that the correlation between
eating quality scores in different muscles within the same
animal are equal. Where this is not the case, this is likely to
result in the analysis being over sensitive with respect to
significant interactions with cut. The degrees of freedom
were determined using the Kenward and Rodger technique.
The consumers were not expected to have much variation
between countries on the basis of previous work (Thompson
et al., 2008; Polkinghorne et al., 2011; Legrand et al., 2013).
Individual base models were determined for the three
different groups of data.
Separately ossification score and age were then incorporated as both single and squared terms into the base models,
including all interactions, to assess their association with the
MQ4. In all cases, non-significant terms (P > 0.05) were
removed in a step-wise fashion. Where ossification score or
age and their interactions remained significant we have
interpreted the magnitude of effect of the covariate on MQ4.
Magnitude of effect was calculated as the difference
between the highest and lowest predicted MQ4 values over
the range of the covariate being examined, with larger values
implying a greater influence of the covariate on MQ4.
A positive value would indicate an increase in MQ4 over the
range of the covariate, while a negative value would indicate
a decrease in MQ4 over the range of the covariate. Following
this the covariates ossification score, marbling score,
ultimate pH, animal age and carcass weight were tested in
the models to evaluate their effects on the relationship
between MQ4 and ossification score and age.
Lesser maturity
Greater maturity
100
0
0
1000
2000
3000
4000
5000
6000
Age
Figure 1 The age (days) of each bovine carcass against the ossification
score, each circle represents a carcass, with the carcasses above and right
of the dotted lines were classified as the greater maturity group;
carcasses to the left and below the dotted lines were classified as the
lesser maturity group. Ossification score was recorded as standard MSA
(Meat Standards Australia) measurements by trained graders.
6
Results
Correlation between maturity measures
The partial correlation coefficients between ossification and
age were strongly positive across all the data, 0.79
(P < 0.001), and within the group with greater maturity, 0.79
(P < 0.001), while being markedly reduced for the carcasses
with lesser maturity, 0.35 (P < 0.01).
The maturity measures were also assessed for their
correlation with MQ4. As ossification score and age are
Ossification is better than age for beef quality
Table 5 Partial correlation coefficients between either the chronological age (days) or the ossification score of bovine carcasses,
and eating quality (MQ4a), overall and by muscle, for three maturity ranges
Full data rangeb
Muscle
Blade1
Chuck2
Chuck Tender3
Cube roll a4
Cube roll b5
Silverside a6
Knuckle a7
Knuckle b8
Silverside b9
Blade10
Rump cap11
Rump tail12
Eye of rump centre13
Eye of rump side14
Shortloin15
Tenderloin16
Topside a17
Topside b18
Average
Lesser maturityc
Greater maturityd
Age
Ossification
Age
Ossification
Age
Ossification
−0.16
−0.14
−0.22
−0.11
−0.32
−0.04
0.03
−0.12
−0.16***
−0.23
0.06
−0.16
−0.10**
−0.04
−0.03
0.00
0.06
−0.03
−0.05***
−0.22*
−0.2
0.05
0.33
0.04
0.00
−0.03
−0.21
−0.20***
−0.39**
0.03
0.29
−0.08*
−0.10*
−0.03
0.05
0.04
−0.04
−0.04***
0.00
0.05
−0.22
−0.11
−0.32
−0.13
−0.06
0.03
−0.06
0.07
0.06
−0.16
0.04
−0.01
0.00
0.08
0.06
0.03
0.00
−0.03
0.10
0.05
0.33
0.04
−0.08
−0.10
−0.11
−0.10*
0.00
0.03
0.29
0.01
−0.09
0.00
0.17**
0.04
0.03
0.01
−0.37
−0.49
–
–
–
–
0.10
0.02
−0.28*
0.01
–
–
−0.28
−0.29
−0.16
−0.15
–
−0.08
−0.12*
−0.67
−0.76
–
–
–
–
−0.04
−0.46
−0.38**
−0.28
–
–
−0.24
−0.36
−0.16
−0.12
–
−0.25*
−0.08
1
m. triceps brachii caput longum; 2m. serratus ventralis cervicis; 3m. supraspinatus; 4m. longissimus thoracis et lumborum; 5m. spinalis dorsi;
m. semitendinosus; 7m. rectus femoris; 8m. vastus lateralis; 9m. biceps femoris; 10m. infraspinatus; 11m. biceps femoris; 12m. tensor fasciae
latae; 13m. gluteus medius; 14m. gluteus medius; 15m. longissimus thoracis et lumborum; 16m. psoas major; 17m. adductor femoris;
18
m. semimembranosus.
a
A weighted combination (0.3, 0.1, 0.3, 0.3) of four sensory scores, tenderness, juiciness, flavour liking and overall liking.
b
All the carcasses.
c
Ossification score ⩽ 200 or age ⩽ 987 days.
d
Carcasses not classified as having lesser maturity.
*P < 0.05; **P < 0.01; ***P < 0.001.
6
measurements of carcasses and MQ4 is a measurement of
muscles, the correlations were also performed for each
muscle tested (Table 5). Where significant (P < 0.05), the
correlation between either of the maturity measures and
MQ4 was negative, except for the correlation between ossification score and MQ4 in the tenderloin for the group with
lesser maturity. On average both age and ossification score
had small negative correlations with MQ4 across all the data.
This average was driven by two muscles for age and five
muscles for ossification score. Neither age nor ossification
score were correlated with MQ4 for the group with lesser
maturity, despite the negative relationship between ossification score and MQ4 for both the tenderloin and the silverside b. For the group with greater maturity only animal
age correlated with MQ4 overall and this was underpinned
by a single muscle, the silverside b. Despite both the silverside and the topside b demonstrating negative correlations
between ossification score and MQ4 for the greater maturity
group, there was no correlation when all muscles were
considered together.
Influence of maturity measures on eating quality
Outcomes for the core model utilizing the full data set
are presented in Table 6, along with this same core model
and either ossification score or age included as covariates.
Both ossification score and age had a significant, negative
relationship with MQ4. Although this effect varied
between cooking methods, in all cases age had a greater
magnitude of effect on MQ4 than ossification (Table 7) with
both covariates demonstrating a negative relationship with
MQ4. When the models were separately corrected for the
covariates ultimate pH, carcass weight, hump height and eye
muscle area (results not shown) ossification and age
remained significant in the model. Correcting for marbling
score also had no impact on any of the other effects within
the core model or the model including ossification score. The
only variation to this theme was for the model including
animal age, the effect of which no longer varied by cooking
method when corrected for marbling score.
When the effect of ossification and age were analysed
separately within the lesser and greater maturity ranges,
their association with MQ4 differed. Within the lesser
maturity group, ossification score had a significant effect on
MQ4, although this effect varied by cooking method and
with postmortem ageing (P < 0.01; Table 8). Alternatively,
age showed no association with MQ4 (Table 8). This result
was not influenced after correcting the model for the
covariates ultimate pH, carcass weight, marbling score,
hump height and eye muscle area (results not shown).
Alternatively, correcting the model for the covariates carcass
weight, hump height and ultimate pH resulted in a more
consistent effect of ossification score whose association with
7
Bonny, Pethick, Legrand, Wierzbicki, Allen, Farmer, Polkinghorne, Hocquette and Gardner
Table 6 F values and degrees of freedom for effects included in the base model predicting the composite eating quality score MQ4a using the full
range of carcass maturity in the data set, and the base model with either the age or ossification score of bovine carcasses included
Core model
Effect
Hang
Sex
Cook
Muscle type
Days aged
Breed class
Days aged × muscle type
Days aged × sex
Cook × muscle type
Hang × muscle type
Muscle type × breed class
Maturity measureb
Maturity measure × cookb
Age
Ossification
NDF
DDF
F value
NDF
DDF
F value
NDF
DDF
F value
1
2
3
14
1
2
13
2
26
10
28
–
–
6616
692
6242
6491
6426
880
6491
6367
6381
6380
6473
–
–
41.5***
5.77**
7.84***
25.2***
0.31
11.4***
5.27***
4.07*
7.72***
17.0***
4.84***
–
–
1
2
3
14
1
2
13
2
26
10
28
1
3
6626
690
6313
6489
6437
988
6488
6392
6381
6380
6473
4784
5894
41.8***
7.02**
3.97**
25.3***
0.53
9.82***
5.32***
3.57*
7.75***
17.1***
4.82***
0.17
2.64*
1
2
3
14
1
2
13
2
26
10
28
1
3
6628
683
6295
6489
6477
1007
6489
6416
6381
6381
6477
3694
6027
42.6***
6.52**
6.52***
25.4***
0.67
10.3***
5.33***
3.78*
7.72***
17.0***
4.84***
0.40
5.80***
NDF = numerator degrees of freedom; DDF = denominator degrees of freedom.
The core model comprised of the following fixed effects and all of their significant interactions; carcass hang method, cooking method, muscle type, sex, country, and
breed class. Postmortem ageing period in days was included as a covariate. Animal identification number, within carcass source country, consumer country and kill group
(animals slaughtered on the same day at the same abattoir) were included as random terms. The covariates age and ossification score were included separately.
a
A weighted combination (0.3, 0.1, 0.3, 0.3) of four sensory scores, tenderness, juiciness, flavour liking and overall liking.
b
Maturity measure = either age or ossification score.
*P < 0.05; **P < 0.01; ***P < 0.001.
Table 7 The magnitude of effecta of either the animal age (days) or ossification score of bovine carcasses on the eating quality
(MQ4) of beef, over the range of either age or ossification by cooking method, for the three different maturity ranges
Allb
Grill
Roast
Slow cook
Korean BBQ
Greater maturityc
Lesser maturityd
Age
Ossification
Age
Ossification
Age
Ossification
−8.36
−3.72
−14.5
1.94
−7.29
−0.84
−13.6
1.05
−5.22
−14.0
−10.8
–
−5.93
−5.93
−5.62
–
–
–
–
–
−8.22
−1.59
−10.6
−1.20
BBQ = barbeque.
a
Magnitude of effect is calculated as the difference between the highest and lowest predicted MQ4 values across the range of ossification or age
in the group examined.
b
All the carcasses.
c
Greater maturity = carcasses not classified as having lesser maturity.
d
Lesser maturity = ossification score ⩽ 200 or age ⩽ 987 days.
MQ4 no longer varied between sexes or carcase source
countries.
Within the greater maturity group both ossification and age
were associated with MQ4 (P < 0.05; Table 9), although the
effect of age varied between cooking methods. When samples
were grilled, ossification score has a greater magnitude of
effect on MQ4 than age (Table 7). When samples were roasted
or slow-cooked, age had a greater magnitude of effect than the
model including ossification score (Table 7). For the most part,
correcting the models for the covariates had no impact on the
magnitude of the association between MQ4 and either ossification or age. The only variation to this theme was for ossification which was no longer significant when the model was
corrected for carcase weight or hump height.
8
Discussion
Correlation between maturity measures
Aligning well with the hypothesis, ossification score and
animal age had strong positive partial correlation coefficients
across all the data and for the group with greater maturity.
Although still positive, the correlation coefficient for the
group with lesser maturity was lower. This supports the work
of others who have investigated the relationship between
ossification score and age within the USDA beef grading
system (Shackelford et al., 1995; Raines et al., 2008) and
supports the idea that the plateau in ossification scores
would reduce the slope of the association between ossification score and age. The strong correlation between these two
Ossification is better than age for beef quality
Table 8 F values and degrees of freedom for effects included in the base model predicting the composite eating quality score MQ4a for bovine
carcasses with lesser maturity, and the base model with either the age or ossification score of bovine carcasses included
Core model
Effect
Age
Ossification
NDF
DDF
F value
NDF
DDF
F value
NDF
DDF
F value
1
2
3
4
14
1
2
13
26
10
27
2
–
–
–
6250
255
5817
3.88
6119
6153
393
6120
6037
6044
6103
256
–
–
–
42.9***
19.5***
7.64***
3.27
21.7***
0.77
4.50*
4.80***
7.44***
15.3***
4.42***
4.17*
–
–
–
1
2
3
4
14
1
2
13
26
10
27
2
–
–
–
6250
255
5817
3.88
6119
6153
393
6120
6037
6044
6103
256
–
–
–
42.9***
19.5***
7.64***
3.27
21.7***
0.77
4.50*
4.80***
7.44***
15.3***
4.42***
4.17*
–
–
–
1
2
3
4
14
1
2
13
26
10
27
2
1
3
1
6248
298
5846
4.22
6103
6124
396
6105
6020
6027
6084
257
2146
5802
6320
44.6***
19.4***
4.26**
3.59
21.7***
0.37
4.76**
4.61***
7.45***
15.3***
4.47***
3.93*
3.20
4.33**
7.19**
Hang
Sex
Cook method
Country
Muscle type
Days aged
Breed class
Days aged × muscle type
Cook × muscle type
Hang × muscle type
Muscle type × breed class
Country × breed class
Maturity measureb
Maturity measure × cookb
Maturity measure × days agedb
NDF = numerator degrees of freedom; DDF = denominator degrees of freedom.
Data set includes carcasses with an ossification score ⩽ 200 or an age ⩽ 987 days; the core model comprised of the following fixed effects and all of their significant
interactions; carcass hanging method, cooking method, muscle type, sex, country, and breed class. Postmortem ageing period in days was included as a covariate.
Animal identification number, within carcass source country, consumer country and kill group (animals slaughtered on the same day at the same abattoir) were included
as random terms. The covariates age and ossification score were included separately.
a
A weighted combination (0.3, 0.1, 0.3, 0.3) of four sensory scores, tenderness, juiciness, flavour liking and overall liking.
b
Maturity measure = either age or ossification score.
*P < 0.05; **P < 0.01; ***P < 0.001.
Table 9 F values and degrees of freedom for effects included in the base model predicting the composite eating quality score MQ4a for bovine
carcasses with greater maturity, and the base model with either the age or ossification score of bovine carcasses included
Core model
Effect
Hang
Cook method
Muscle type
Breed class
Cook × cut
Cut × breed class
Maturity measureb
Maturity measure × cookb
Maturity measure2b
Maturity measure2 × cookb
Age
Ossification
NDF
DDF
F value
NDF
DDF
F value
NDF
DDF
F value
1
2
10
2
10
3
–
–
–
–
334
340
332
144
332
357
–
–
–
–
11.9***
14.5***
39.6***
1.59
2.61**
7.77***
–
–
–
–
1
2
10
2
10
3
1
2
1
2
344
346
327
143
327
354
96.2
341
260
338
10.4**
3.51*
40.3***
2.05
2.54**
6.96***
6.96**
3.07*
2.93
3.77*
1
2
10
2
10
3
1
–
–
–
342
333
330
137
331
355
42.1
–
–
–
9.9**
14.1***
39.6***
2.06
2.61**
7.79***
4.47*
–
–
–
NDF = numerator degrees of freedom; DDF = denominator degrees of freedom.
Data set includes carcasses with an ossification score ⩽ 200 or an age ⩽987 days; the core model comprised of the following fixed effects and all of their significant
interactions; carcass hanging method, cooking method, muscle type, sex, country and breed class. Postmortem ageing period in days was included as a covariate. Animal
identification number, within carcass source country, consumer country and kill group (animals slaughtered on the same day at the same abattoir) were included as
random terms. The covariates age and ossification score were included separately.
a
A weighted combination (0.3, 0.1, 0.3, 0.3) of four sensory scores, tenderness, juiciness, flavour liking and overall liking.
b
Maturity measure = either age or ossification score.
*P < 0.05; **P < 0.01; ***P < 0.001.
measurements indicates that they are likely to have similar
relationships with eating quality. This plateau in ossification
score appeared in this data set at about 8 years of age
(3000 days). The same plateau was also noted by Raines
et al. (2008) at 8 years of age and would explain the smaller
slope of the relationship between age and ossification score
when carcasses of greater maturity are assessed. Any small
differences in the strength of either measure with eating
quality would likely be outweighed by other factors such as
cost and convenience in any one industry.
This result for the lesser maturity group shows that the
strength of the relationship between these measures of
9
Bonny, Pethick, Legrand, Wierzbicki, Allen, Farmer, Polkinghorne, Hocquette and Gardner
maturity is not consistent over different maturity ranges.
Other researchers have also expressed concerns over the
ability for ossification score to act as a proxy for animal
age across varying maturity ranges and diverse production
systems within the United States (Field et al., 1997; Lawrence
et al., 2001). Studies that find strong relationships between
ossification score and age often source from relatively
standard and consistent production systems (Shackelford
et al., 1995). In light of this uncertainty it is important to
determine the most appropriate maturity measure for the
prediction of eating quality across the European production
environment.
Maturity measures and eating quality
Animal age had slightly greater magnitude of effect on MQ4
when all of the data was considered, giving the largest range
in maturity. Likewise, the correlation coefficients also supported this general theme of age having a slightly stronger
correlation with MQ4 when assessed across all the data,
although these correlations were small and quite variable
within individual muscles. Aligning well with our hypothesis,
this trend was also evident when the group with greater
maturity was assessed. Importantly, these results support the
general theme of a negative relationship between animal
maturity and eating quality (Bailey, 1985), but also indicate
the potential for animal age to be used as the indicator of
this effect. Furthermore, they highlight that when assessing
carcasses of greater maturity the utility of ossification score is
limited since when ossification is completed at around
8 years of age, the maximum score of 590 is reached (USDA,
1997).
Also aligning with our hypothesis ossification score had a
greater magnitude of effect on eating quality than animal
age for carcasses with lesser maturity. Indeed animal age
was found to have no significant effect at all. This supports
the notion that ossification score more closely relates to
physiological maturity (Field et al., 1997) and therefore age
related decreases in eating quality (Bailey, 1985). This
finding is in contrast to the outcome for the greater maturity
group where animal age was more strongly associated with
eating quality than ossification, likely due to the insensitivity
of ossification beyond 8 years of age. The lack of an age
effect in the lesser maturity group is also supported by Field
et al. (1966) who concluded that animal age is not a
significant determinant of eating quality in animals less than
two years old. The correlation coefficients within the lesser
maturity group partly supported these findings in that there
was no significant correlation for age v. MQ4, yet there was
also no correlation for ossification v. MQ4.
When the model including ossification score for the lesser
maturity group was corrected for the phenotypic traits;
carcass weight, hump height, marbling score and ultimate
pH, ossification score no longer varied by carcass source
country and/or sex. This may indicate that some of the
variation in the relationship between MQ4 and ossification
score is likely due to differences in the phenotype of the
animals between countries and genders.
10
These phenotypic corrections also affected the model
which included ossification score for the greater maturity
group. When either carcass weight or eye muscle area were
included in the model, ossification score was no longer significant. This is not entirely surprising given that weight and
eye muscle area are both strongly correlated with maturity,
hence they explain similar sources of variation in eating
quality. This may also imply that age or ossification may
therefore be of limited use in a processing environment that
routinely collects carcase weight or eye muscle area.
Overall the results have shown that the best maturity
measurement depends on the expected maturity of the cattle
to be evaluated. Animal age would be more useful for
predicting the eating quality of mature animals such as cull
cows and bulls that are likely to reach the maximum ossification score. However, age would not be useful for young
bulls, steers and heifers produced in a more conventional
beef production system, with ossification score being a more
suitable maturity measure. Additional information in
populations where parity status was known and a greater
volume of data on carcasses of advanced maturity would be
needed to fully confirm this conclusion.
Conclusion
Delivering a price signal on eating quality is a good incentive
for producers to deliver carcasses that have a better and more
consistent eating quality. Maturity-related decreases in eating
quality are estimated by either animal age or ossification score.
The strength of the relationship between these measures and
eating quality varies with different carcass maturity ranges.
Ossification score is more appropriate for younger carcasses
more commonly used for production, however for more mature
animals, such as cull cows animal age becomes a more accurate predictor of eating quality. This indicates that a combination of animal age and ossification score would be required to
adequately guarantee beef eating quality to consumers across
the diversity of the European beef production system.
Acknowledgements
This research was supported by Meat and Livestock Australia and
Murdoch University. Data were obtained through the financial
contributions of the European research project ProSafeBeef
(Contract No. FOOD-CT-2006-36241), the Polish ProOptiBeef
Farm to Fork project funded by the EU Innovative (POIG.01.03.0100-204/09), the French ‘Direction Générale de l’Alimentation’ and
FranceAgriMer, the Irish Department of Agriculture Food and The
Marine under the FIRM programme and the Northern Ireland
Department of Agriculture and Rural Development ‘Vision’ programme. Furthermore, this project would not have been possible
without the practical support of the Association Institut du
Charolais, the Syndicat de Défense et du promotion de la Viande
de Boeuf de Charolles and the gourmet restaurants ‘Jean Denaud’
and representatives of the beef industry across Europe. The
international travel required for this project has been funded by
‘Egide/Fast’ funds from the French and Australian governments,
Ossification is better than age for beef quality
respectively (project no. FR090054) and by ‘Egide/Polonium’ funds
from the French and Polish governments, respectively. The assistance and participation the Beef CRC and Janine Lau (MLA,
Australia), Alan Gee (Cosign, Australia), Ray Watson (Melbourne
University, Australia) and John Thompson (UNE) are also
gratefully acknowledged.
Polkinghorne R, Watson R, Thompson JM and Pethick DW 2008a. Current usage
and future development of the Meat Standards Australia (MSA) grading system.
Australian Journal of Experimental Agriculture 48, 1459–1464.
Polkinghorne R, Thompson JM, Watson R, Gee A and Porter M 2008b. Evolution
of the Meat Standards Australia (MSA) beef grading system. Australian Journal
of Experimental Agriculture 48, 1351–1359.
AUS-MEAT 2005. Handbook of Australian Meat. AUS-MEAT Ltd, Brisbane.
Polkinghorne RJ, Nishimura T, Neath KE and Watson R 2011. Japanese
consumer categorisation of beef into quality grades, based on Meat Standards
Australia methodology. Animal Science Journal 82, 325–333.
Raines CR, Dikeman ME, Unruh JA, Hunt MC and Knock RC 2008. Predicting
cattle age from eye lens weight and nitrogen content, dentition, and United
States Department of Agriculture maturity score. Journal of Animal Science 86,
3557–3567.
Anonymous 2008. Accessory Publication MSA sensory testing protocols. From:
Watson R, Polkinghorne R and Thompson JM 2008. Development of the Meat
Standards Australia (MSA) prediction model for beef palatability. Australian
Journal of Experimental Agriculture 48, 1368–1379.
Scheffler JM, Buskirk DD, Rust SR, Cowley JD and Doumit ME 2003. Effect of
repeated administration of combination trenbolone acetate and estradiol
implants on growth, carcass traits, and beef quality of long-fed Holstein steers.
Journal of Animal Science 81, 2395–2400.
Bailey AJ 1985. The role of collagen in the development of muscle and its
relationship to eating quality. Journal of Animal Science 60, 1580–1587.
Schonfeldt HC and Strydom PE 2011. Effect of age and cut on tenderness of
South African beef. Meat Science 87, 206–218.
Bonny SP, Gardner GE, Pethick DW, Legrand I, Polkinghorne RJ and Hocquette J-F
2015. Biochemical measurements of beef are a good predictor of untrained
consumer sensory scores across muscles. Animal 9, 179–190.
Shackelford SD, Koohmaraie M and Wheeler TL 1995. Effects of slaughter age on
meat tenderness and USDA carcass maturity scores of beef females. Journal of
Animal Science 73, 3304–3309.
Field R, McCormick R, Balasubramanian V, Sanson D, Wise J, Hixon D, Riley M
and Russell W 1996. Growth, carcass, and tenderness characteristics of virgin,
spayed, and single-calf heifers. Journal of Animal Science 74, 2178–2186.
Shorthose WR and Harris PV 1990. Effect of animal age on the tenderness of
selected beef muscles. Journal of Food Science 55, 1–8.
References
Field R, McCormick R, Balasubramanian V, Sanson D, Wise J, Hixon D, Riley M
and Russell W 1997. Tenderness variation among loin steaks from A and C
maturity carcasses of heifers similar in chronological age. Journal of Animal
Science 75, 693–699.
Field RA, Nelms GE and Schoonover CO 1966. Effects of age, marbling and sex
on palatability of beef. Journal of Animal Science 25, 360–366.
Lawrence TE, Whatley JD, Montgomery TH and Perino LJ 2001. A comparison of
the USDA ossification-based maturity system to a system based on dentition.
Journal of Animal Science 79, 1683–1690.
Legrand I, Hocquette J-F, Polkinghorne RJ and Pethick DW 2013. Prediction of
beef eating quality in France using the Meat Standards Australia system. Animal
7, 524–529.
Lyford C, Thompson J, Polkinghorne R, Miller M, Nishimura T, Neath K, Allen P
and Belasco E 2010. Is willingness to pay (WTP) for beef quality grades affected
by consumer demographics and meat consumption preferences? Australasian
Agribusiness Review 18, 1–17.
MLA 2006. Module 6: Meat Standards Australia Grading. version 5.0, pp. 1–111.
Meat &Livestock Australia, Sydney.
Morgan JB, Savell JW, Hale DS, Miller RK, Griffin DB, Cross HR and Shackelford SD
1991. National beef tenderness survey. Journal of Animal Science 69, 3274–3283.
Thompson JM, Polkinghorne R, Hwang IH, Gee AM, Cho SH, Park BY and Lee JM
2008. Beef quality grades as determined by Korean and Australian consumers.
Australian Journal of Experimental Agriculture 48, 1380–1386.
USDA 1997. United States standards for grades of carcass beef. United States
Department of Agriculture, Washington, DC, USA.
Verbeke W, Van Wezemael L, de Barcellos MD, Kügler JO, Hocquette J-F,
Ueland Ø and Grunert KG 2010. European beef consumers’ interest in a beef
eating-quality guarantee: Insights from a qualitative study in four EU countries.
Appetite 54, 289–296.
Waggoner AW, Dikeman ME, Brethour JR and Kemp KE 1990. Performance,
carcass, cartilage calcium, sensory and collagen traits of longissimus muscles of
open versus 30-month-old heifers that produced one calf. J Journal of Animal
Science 68, 2380–2386.
Watson R, Polkinghorne R and Thompson JM 2008a. Development of the Meat
Standards Australia (MSA) prediction model for beef palatability. Australian
Journal of Experimental Agriculture 48, 1368–1379.
Watson R, Gee A, Polkinghorne R. and Porter M 2008b. Consumer assessment of
eating quality – development of protocols for Meat Standards Australia (MSA)
testing. Australian Journal of Experimental Agriculture 48, 1360–1367.
Weston AR, Rogers RW and Althen TG 2002. Review: the role of collagen in
meat tenderness. Professional Animal Scientist 18, 107–111.
11